Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

Performance and Detection of M-ary Frequency Shift Keying in Triple Layer Wireless Sensor Network

This paper proposes an innovative triple layer Wireless Sensor Network (WSN) system, which monitors M-ary events like temperature, pressure, humidity, etc. with the help of geographically distributed sensors. The sensors convey signals to the fusion centre using M-ary Frequency Shift Keying (MFSK)modulation scheme over independent Rayleigh fading channels. At the fusion centre, detection takes place with the help of Selection Combining (SC) diversity scheme, which assures a simple and economical receiver circuitry. With the aid of various simulations, the performance and efficacy of the system has been analyzed by varying modulation levels, number of local sensors and probability of correct detection by the sensors. The study endeavors to prove that triple layer WSN system is an economical and dependable system capable of correct detection of M-ary events by integrating frequency diversity together with antenna diversity.Comment: 13 pages; International Journal of Computer Networks & Communications (IJCNC) Vol.4, No.4, July 201

Fault-tolerant Coverage in Dense Wireless Sensor Networks

In this paper, we present methods to detect and recover from sensor failure in dense wireless sensor networks. In order to extend the lifetime of a sensor network while maintaining coverage, a minimal subset of the deployed sensors are kept active while the other sensors can enter a low power sleep state. Several distributed algorithms for coverage have been proposed in the literature. Faults are of particular concern in coverage algorithms since sensors go into a sleep state in order to conserve battery until woken up by active sensors. If these active sensors were to fail, this could lead to lapses in coverage that are unacceptable in critical applications. Also, most algorithms in the literature rely on an active sensor that is about to run out of battery waking up its neighbors to trigger a reshuffle in the network. However, this would not work in the case of unexpected failures since a sensor cannot predict the occurrence of such an event. We present detection and recovery from sensor failure in dense networks. Our algorithms exploit the density in the recovery scheme to improve coverage by 4-12% in the event of random failures. This fault tolerance comes at a small cost to the network lifetime with observed lifetime being reduced by 6-10% in our simulation studies